Natural Language Inference with a Human Touch: Using Human Explanations to Guide Model Attention
By Joe Stacey, Yonatan Belinkov, Marek Rei. 2021
10 citations recorded by the source corpus.
Subfields: Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Thomas Lukasiewicz
- Oana-Maria Camburu
- Sebastian Gehrmann
- Yi Tay
- Mohit Bansal
- Kyunghyun Cho
- Julian McAuley
- Quoc V. Le
Citations in the corpus
- Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them (2022, methodology): “For example, providing reasoning paths after the answer has been used to improve model explainability (Zhou et al., 2020; Stacey et al., 2021; Marasović et al., 2022; Mishra et al., 2022; Hase and Bansal, 2022; Wiegreffe et al., 2022; Lampinen et al., 2022).”
- Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations (2021, background): “Moreover, self-explanatory models may benefit from the rich information in the explanations provided at training time (Schramowski et al., 2020; Stacey et al., 2022; Lazaridou et al., 2022).”